In production workflows, it is computationally impractical to use traditional active learning strategies that select a single sample at each step, and retrain the model each time. This approach uses prior work of
Core-set [1]}, where given a fixed annotation budget, the goal is to select a batch subset of B samples that would provide maximal model performance lift. More precisely, the geometry of datapoints is used to pick K well-distributed cores, and these cores are then used to pick B samples. The approach optimizes picking a subset that will be competitive for the remaining datapoints. So, given an initial set of samples and their embeddings, core-set selection returns a subset of samples that should be used to expand the training data.
